Impact Analysis for Rossmann Store Sales

This phase focuses on visualizing temporal patterns in customer behavior and sales performance, specifically analyzing daily and monthly trends across stores. The goal is to uncover seasonality and peak periods. Unlike other analytical stages, this phase does not involve correlation studies—instead, it concentrates on trend-based insights that highlight how customer volume and sales evolve over time, laying the groundwork for more targeted forecasting and strategic planning.

1. Setup & Imports Libraries

---

import time 

# Step 1: Setup & Imports Libraries
print("Step 1: Setup and Import Libraries started...")
time.sleep(1)  # Simulate processing time

Step 1: Setup and Import Libraries started...

# Data Manipulation & Processing
import math
import numpy as np
import pandas as pd
from pathlib import Path
import scipy.stats as stats
from datetime import datetime
from sklearn.preprocessing import *

# Data Visualization
import seaborn as sbn
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

from pandas.plotting import scatter_matrix

# to ensure Plotly works in both Jupyter and HTML export
pio.renderers.default = "notebook+plotly_mimetype"

sbn.set(rc={'figure.figsize':(14,6)})
plt.style.use('seaborn-v0_8')
sbn.set_palette("husl")

# Set display options
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
pd.set_option('display.float_format','{:.2f}'.format)

# Warnings
import warnings
warnings.simplefilter('ignore')
warnings.filterwarnings('ignore')

print("="*60)
print("Rossman Store Sales Time Series Analysis - Part 2")
print("="*60)
print("All libraries imported successfully!")
print("Analysis Date:", pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S'))

============================================================
Rossman Store Sales Time Series Analysis - Part 2
============================================================
All libraries imported successfully!
Analysis Date: 2025-08-15 20:47:39

print("✅ Setup and Import Liraries completed.\n")

✅ Setup and Import Liraries completed.

# Start analysis

data_viz_begin = pd.Timestamp.now()

bold_start = '\033[1m'
bold_end = '\033[0m'

print("🔍 Part 2 Started ...")
print(f"🟢 Begin Date: {bold_start}{data_viz_begin.strftime('%Y-%m-%d %H:%M:%S')}{bold_end}\n")

🔍 Part 2 Started ...
🟢 Begin Date: 2025-08-15 20:47:39

Restore the file

---

%store -r df_viz_feat

View or Display Dataset

print("\nTrain Data Preview:")
print("\n",df_viz_feat.head())

Train Data Preview:

         store  dayofweek       date  sales  customers  open     promo stateholiday  schoolholiday  day  week month  quarter  year  isweekend  isholiday  isschoolDay
982643   1115          2 2013-01-01      0          0     0  No Promo       Public              1  Tue     1   Jan        1  2013      False       True        False
982640   1112          2 2013-01-01      0          0     0  No Promo       Public              1  Tue     1   Jan        1  2013      False       True        False
982639   1111          2 2013-01-01      0          0     0  No Promo       Public              1  Tue     1   Jan        1  2013      False       True        False
982638   1110          2 2013-01-01      0          0     0  No Promo       Public              1  Tue     1   Jan        1  2013      False       True        False
982637   1109          2 2013-01-01      0          0     0  No Promo       Public              1  Tue     1   Jan        1  2013      False       True        False

2. Data Visualization

---

# Step 1: Setup & Imports Libraries
print("Step 2: Data Visualization started...")
time.sleep(1)  # Simulate processing time

Step 2: Data Visualization started...

Percentage Distribution per Open

# Count frequency of each unique value
value_counts = df_viz_feat['open'].value_counts()

# Map numeric labels to descriptive ones using if-else logic
labels = ["Open" if val == 1 else "Closed" for val in value_counts.index]
values = value_counts.values.tolist()

# Dynamically create 'pull' values to highlight the largest slice
pull = [0.1 if i == 0 else 0 for i in range(len(labels))]

# Create pie chart
fig = go.Figure(data=[go.Pie(
    labels=labels,
    values=values,
    pull=pull,
    textinfo='percent+label',
    hoverinfo='label+value+percent'
)])

# Update layout with left-aligned title
fig.update_layout(
    title_text='📊 Store Status Distribution: Open vs Closed',
    title_x=0.0,  # Left-aligned title
    showlegend=True,
    width=1200,    # Increased width
    height=450    # Increased height
)

fig.show(config={'displayModeBar': True, 'displaylogo': False})

# export_plotly_chart(fig, name="open_vs_closed_pie")

Percentage Distribution with Respect to Promo

# Get value counts (Series), extracting both labels and values in matching order
counts = df_viz_feat['promo'].value_counts()
labels = counts.index.tolist()
values = counts.values.tolist()

# Create a 'pull' list to highlight the first class, rest unpulled
pull = [0.1 if i == 0 else 0 for i in range(len(labels))]

# Create the pie chart
fig = go.Figure(
    data =[go.Pie(
        labels = labels,
        values = values,
        pull = pull,
        textinfo ='percent+label',
        insidetextorientation ='radial',
    )]
)

# Update the layout to fix title overlap
fig.update_layout(
    title_text ='📊 % Distribution per Promo',
    title_x = 1.0,  # Centered title
    title_font_size = 20,  # Optional: adjust font size
    margin = dict(t = 60, r = 60, b = 60, l = 60),  # Increased top margin
    width = 1200,
    height = 450
)

fig.show(config={'displayModeBar': True, 'displaylogo': False})

# export_plotly_chart(fig, name="promo_pie")

Percentage Distribution per Holiday Type

# Get value counts (Series), extracting both labels and values in matching order
counts = df_viz_feat['stateholiday'].value_counts()
labels = counts.index.tolist()
values = counts.values.tolist()

# Create a 'pull' list to highlight the first class, rest unpulled
pull = [0.1 if i == 0 else 0 for i in range(len(labels))]

# Create the pie chart
fig = go.Figure(
    data =[go.Pie(
        labels = labels,
        values = values,
        pull = pull,
        textinfo ='percent+label',
        insidetextorientation ='radial',
    )]
)

# Update the layout to fix title overlap
fig.update_layout(
    title_text ='📊 % Distribution per State Holiday',
    title_x = 1.0,  # Centered title
    title_font_size = 20,  # Optional: adjust font size
    margin = dict(t = 60, r = 60, b = 60, l = 60),  # Increased top margin
    width = 1200,
    height = 450
)

fig.show(config={'displayModeBar': True, 'displaylogo': False})

# export_plotly_chart(fig, name="sateholiday_pie")

Percentage Distribution per School Holiday

# Efficient value counting and labeling
counts = df_viz_feat['schoolholiday'].value_counts().sort_index()
labels = ['No School Holiday', 'School Holiday']  # Assumes 0=no, 1=yes
values = counts.values

# Create donut chart
fig = go.Figure(data=go.Pie(
    labels=labels,
    values=values,
    pull=[0, 0.1],  # Emphasize 'School Holiday' slice
    hole=0.3,
    textinfo='label+percent',
    marker=dict(colors=['#4ECDC4', '#FF6B6B'], line=dict(color='#FFFFFF', width=2))
))

# Layout for clarity and aesthetics
fig.update_layout(
    title_text = '📊 % Distribution per School Holiday',
    title_x = 0.5,
    font = dict(size = 12, family = 'Arial, sans-serif'),
    margin = dict(t = 60, b = 40, l = 40, r = 100),
    width = 1200,
    height = 450,
    showlegend  =True
)

# Annotation for total records
fig.add_annotation(
    text = f"Total Records: {len(df_viz_feat):,}",
    x = 0.5,
    y =- 0.1,
    xref ="paper",
    yref = "paper",
    showarrow = False,
    font = dict(size = 10, color="gray")
)

fig.show(config={'displayModeBar': True, 'displaylogo': False})

# export_plotly_chart(fig, name="schoolholiday_pie")

Customer Analysis

Average Customers Trend per Day

# Define weekday order
weekday_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

# Calculate mean sales by day
dow_agg = df_viz_feat.groupby('day')['customers'].mean().reset_index()

# Apply categorical ordering
dow_agg['day'] = pd.Categorical(dow_agg['day'], categories=weekday_order, ordered=True)
dow_agg = dow_agg.sort_values('day')

# Create simple line chart
fig = px.line(
    dow_agg, 
    x = 'day', 
    y ='customers', 
    title ='📈 Average Customers by Day of Week',
    markers = True
)

# Simple styling
fig.update_layout(
    title={'x': 0.5, 'xanchor': 'center'},
    xaxis_title ='Day of Week',
    yaxis_title ='Average Customers',
)

# Find peak for simple annotation
peak_idx = dow_agg['customers'].idxmax()
peak_day = dow_agg.loc[peak_idx, 'day']
peak_value = dow_agg.loc[peak_idx, 'customers']

# Simple annotation
fig.add_annotation(
    x = peak_day,
    y = peak_value,
    text = f"Peak: {peak_day} ({peak_value:.0f})",
    showarrow = True,
    arrowcolor ='red',
    font = dict(color = 'red')
)

fig.update_layout(margin=dict(t=60, r=40, b=40, l=40),  width = 1200,height = 400)
fig.show(config={'displayModeBar': True, 'displaylogo': False})

print("Daily Sales Data:")
print(dow_agg)

# export_plotly_chart(fig, name="daily_avg_customers_trend")

Daily Sales Data:
   day  customers
1  Mon     812.93
5  Tue     761.86
6  Wed     721.20
4  Thu     695.78
0  Fri     742.53
2  Sat     658.76
3  Sun      35.58

# Ensure month is ordered
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun','Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

monthly_grp = df_viz_feat.groupby('month', as_index=False)['customers'].mean()
monthly_grp['month'] = pd.Categorical(monthly_grp['month'], categories=month_order, ordered=True)
monthly_grp = monthly_grp.sort_values('month')

# Identify peak
peak_row = monthly_grp.loc[monthly_grp['customers'].idxmax()]
peak_month = peak_row['month']
peak_value = peak_row['customers']

# Create Plotly Express line plot
fig = px.line(monthly_grp, x='month', y='customers',
              markers=True,
              title='📈  Average Customers Trend per Month',
              labels={'customers': 'Average Customers', 'month': 'Month'},
              line_shape='linear')  # You can switch to 'spline' for smooth curves

# Annotate peak
fig.add_annotation(x=peak_month, y=peak_value,
                   text=f'Peak: {peak_month} ({peak_value:.1f})',
                   showarrow=True,
                   arrowhead=2,
                   arrowsize=1,
                   arrowwidth=2,
                   arrowcolor='red',
                   font=dict(color='red', size=12),
                   yshift=15)

fig.update_layout(margin=dict(t=60, r=40, b=40, l=40),width = 1200,height = 450)
fig.show(config={'displayModeBar': True, 'displaylogo': False})

# export_plotly_chart(fig, name="monthly_avg_customers_trend")

Average Customers Trend per Month

Sales Analysis

Average SalesTrend per Day

# Define weekday order
weekday_order = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']

# Calculate mean sales by day
dow_agg = df_viz_feat.groupby('day')['sales'].mean().reset_index()

# Apply categorical ordering
dow_agg['day'] = pd.Categorical(dow_agg['day'], categories=weekday_order, ordered=True)
dow_agg = dow_agg.sort_values('day')

# Create simple line chart
fig = px.line(
    dow_agg, 
    x = 'day', 
    y ='sales', 
    title ='📈 Average Sales by Day of Week',
    markers = True
)

# Simple styling
fig.update_layout(
    title={'x': 0.5, 'xanchor': 'center'},
    xaxis_title ='Day of Week',
    yaxis_title ='Average Sales',
)

# Find peak for simple annotation
peak_idx = dow_agg['sales'].idxmax()
peak_day = dow_agg.loc[peak_idx, 'day']
peak_value = dow_agg.loc[peak_idx, 'sales']

# Simple annotation
fig.add_annotation(
    x = peak_day,
    y = peak_value,
    text = f"Peak: {peak_day} ({peak_value:.0f})",
    showarrow = True,
    arrowcolor ='red',
    font = dict(color = 'red')
)

fig.update_layout(margin=dict(t=60, r=40, b=40, l=40), width = 1200,height = 400)
fig.show(config={'displayModeBar': True, 'displaylogo': False})

# export_plotly_chart(fig, name="daily_avg_sales_trend")

Average Sales Trend per Month

# Ensure month is ordered
month_order = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun','Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']

monthly_grp = df_viz_feat.groupby('month', as_index=False)['sales'].mean()
monthly_grp['month'] = pd.Categorical(monthly_grp['month'], categories=month_order, ordered=True)
monthly_grp = monthly_grp.sort_values('month')

# Identify peak
peak_row = monthly_grp.loc[monthly_grp['sales'].idxmax()]
peak_month = peak_row['month']
peak_value = peak_row['sales']

# Create Plotly Express line plot
fig = px.line(monthly_grp, x='month', y='sales',
              markers=True,
              title='📈  Average Sales Trend per Month',
              labels={'sales': 'Average Sales', 'month': 'Month'},
              line_shape='linear')  # You can switch to 'spline' for smooth curves

# Annotate peak
fig.add_annotation(x=peak_month, y=peak_value,
                   text=f'Peak: {peak_month} ({peak_value:.1f})',
                   showarrow=True,
                   arrowhead=2,
                   arrowsize=1,
                   arrowwidth=2,
                   arrowcolor='red',
                   font=dict(color='red', size=12),
                   yshift=15)

fig.update_layout(margin=dict(t=60, r=40, b=40, l=40),width = 1200,height = 450)
fig.show(config={'displayModeBar': True, 'displaylogo': False})

# export_plotly_chart(fig, name="montly_avg_sales_trend")

### Promotion Impact Analysis

# Single comprehensive chart showing promotion effectiveness
promo_summary = df_viz_feat.groupby(['day', 'promo']).agg({
    'sales': 'mean',
    'customers': 'mean'
}).reset_index()

fig = px.bar(
    promo_summary,
    x='day',
    y='sales',
    color='promo',
    title='Promotion Impact: Average Sales by Day',
    color_discrete_map={'Promo': '#636EFA', 'No Promo': '#EF553B'},
    barmode='group'
)

fig.update_layout(
    title_x=0.5,
    height=450,
    width=1200
)

fig.show()

# Complete promotion analysis in one summary
print("Complete Promotion Impact Analysis:")
print("=" * 50)

for day in sorted(df_viz_feat['day'].unique()):
    no_promo = df_viz_feat[(df_viz_feat['day'] == day) & (df_viz_feat['promo'] == 'No Promo')]['sales'].mean()
    promo = df_viz_feat[(df_viz_feat['day'] == day) & (df_viz_feat['promo'] == 'Promo')]['sales'].mean()
    lift = ((promo - no_promo) / no_promo) * 100 if not pd.isna(no_promo) and not pd.isna(promo) else 0
    
    print(f"{day:9}: No Promo €{no_promo:6,.0f} | Promo €{promo:6,.0f} | Lift {lift:+5.1f}%")

# Overall summary
overall_no_promo = df_viz_feat[df_viz_feat['promo'] == 'No Promo']['sales'].mean()
overall_promo = df_viz_feat[df_viz_feat['promo'] == 'Promo']['sales'].mean()
overall_lift = ((overall_promo - overall_no_promo) / overall_no_promo) * 100

print(f"\nOverall Impact:")
print(f"Average lift from promotions: {overall_lift:+.1f}%")
print(f"Best day: {day} with highest visual bar difference")
print(f"Promo ROI: €{overall_promo - overall_no_promo:,.0f} extra per day")

print("✅ Data Visualization completed.\n")

✅ Data Visualization completed.

---

print("✅ Features Engineering and Data Visualization (I) completed successfully!")
print(f"🗓️ Analysis Date: {bold_start}{pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')}{bold_end}")

✅ Features Engineering and Data Visualization (I) completed successfully!
🗓️ Analysis Date: 2025-08-15 20:47:41

# End analysis
data_viz_end = pd.Timestamp.now()
duration = data_viz_end - data_viz_begin

# Final summary print
print("\n📋 Features Engineering && Data Viz Summary")
print(f"🟢 Begin Date: {bold_start}{data_viz_begin.strftime('%Y-%m-%d %H:%M:%S')}{bold_end}")
print(f"✅ End Date:   {bold_start}{data_viz_end.strftime('%Y-%m-%d %H:%M:%S')}{bold_end}")
print(f"⏱️ Duration:   {bold_start}{str(duration)}{bold_end}")

📋 Features Engineering && Data Viz Summary
🟢 Begin Date: 2025-08-15 20:47:39
✅ End Date:   2025-08-15 20:47:41
⏱️ Duration:   0 days 00:00:02.457407

---

Project Design Rationale: Notebook Separation

To ensure clarity, maintainability, and scalability while adhering to GitHub's file size limitations, each .ipynb notebook should be modularized by task—allowing for streamlined version control, easier collaboration, and more efficient long-term project management.